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The optimisation model for groundwater management in the unconfined aquifer using the Shuffled Complex Evolution algorithm

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Groundwater exploitation that exceeds its recharge capacity can have a negative impact on the hydrogeological environment. Optimal exploitation means maximising pumping discharge with the least reduction in the hydraulic head. In groundwater exploitation, the position of wells, number of wells, and the discharge of groundwater pumping greatly determine changes in hydraulic head and groundwater flow patterns in a given hydrological area. This article proposes an optimisation model which is expected to be useful for finding the optimal pumping discharge value from production wells in a hydrological area. This model is a combination of solving the Laplace equation for two-dimensional groundwater flow in unconfined aquifers and the optimum variable search method based on the Shuffled Complex Evolution (SCE-UA) algorithm. Laplace equation uses the finite difference method for the central difference rule of the Crank Nicolson scheme. The system of equations has been solved using the M-FILE code from MATLAB. This article is a preliminary study which aims to examine the stability level of the optimisation equation system. Testing using a hypothetical data set shows that the model can work effectively, accurately, and consistently in solving the case of maximising pumping discharge from production wells in a hydrological area with a certain hydraulic head limitation. Consequently, the system of equations can also be applied to the case of confined aquifers.
Wydawca
Rocznik
Tom
Strony
83--92
Opis fizyczny
Bibliogr. 23 poz., rys., tab., wykr.
Twórcy
autor
  • University of Muhammadiyah Malang, Department of Civil Engineering, Jl. Raya Tlogomas No. 246, 65114, Malang, Indonesia
  • University of Muhammadiyah Malang, Department of Civil Engineering, Jl. Raya Tlogomas No. 246, 65114, Malang, Indonesia
autor
  • University of Muhammadiyah Malang, Department of Civil Engineering, Jl. Raya Tlogomas No. 246, 65114, Malang, Indonesia
  • University of Muhammadiyah Malang, Department of Civil Engineering, Jl. Raya Tlogomas No. 246, 65114, Malang, Indonesia
  • University of Muhammadiyah Malang, Department of Civil Engineering, Jl. Raya Tlogomas No. 246, 65114, Malang, Indonesia
Bibliografia
  • Adebayo, S.O and Abraham, A.A. (2018) “Aquifer, classification and characterization,” in M.S. Javaid and S.A. Khan (eds.) Aquifers – matrix and fluids. IntechOpen. Available at: https://doi.org/10.5772/intechopen.72692.
  • Alaviani, F. et al. (2018) “Adopting GMS–PSO model to reduce groundwater withdrawal by integrated water resources management,” International Journal of Environmental Research, 12(5), pp. 619–629. Available at: https://doi.org/10.1007/s41742-018-0115-x.
  • Ayvaz, M.T. (2009) “Application of harmony search algorithm to the solution of groundwater management models,” Advances in Water Resources, 32(6), pp. 916–924. Available at: https://doi.org/10.1016/j.advwatres.2009.03.003.
  • Duan, Q., Gupta, V.K. and Sorooshian, S. (1993) “Shuffled complex evolution approach for effective and efficient global minimization,” Journal of Optimization Theory and Applications, 76(3), pp. 501–521. Available at: https://doi.org/10.1007/bf00939380.
  • Duan, Q., Sorooshian, S. and Gupta, V.K. (1992) “Effective and efficient global optimization for conceptual rainfall-runoff models,” Water Resources Research, 28(4), pp. 1015–1031. Available at: https://doi.org/10.1029/91wr02985.
  • Duan, Q., Sorooshian, S. and Gupta, V.K. (1994) “Optimal use of the SCE-UA global optimization method for calibrating watershed models,” Journal of Hydrology, 158(3–4), pp. 265–284. Available at: https://doi.org/10.1016/0022-1694(94)90057-4.
  • Gaur, S., Chahar, B.R. and Graillot, D. (2011) “Analytic elements method and particle swarm optimization based simulation–optimization model for groundwater management,” Journal of Hydrology, 402(3–4), pp. 217–227. Available at: https://doi.org/10.1016/j.jhydrol.2011.03.016.
  • Gaur, S. et al. (2012) “Application of artificial neural networks and particle swarm optimization for the management of groundwater resources,” Water Resources Management, 27(3), pp. 927–941. Available at: https://doi.org/10.1007/s11269-012-0226-7.
  • Gorelick, S.M. and Zheng, C. (2015) “Global change and the groundwater management challenge,” Water Resources Research, 51(5), pp. 3031–3051. Available at: https://doi.org/10.1002/2014wr016825.
  • Haddad, O.B. et al. (2013) “Groundwater model calibration by metaheuristic algorithms,” Water Resources Management, 27(7), pp. 2515–2529. Available at: https://doi.org/10.1007/s11269-013-0300-9.
  • He, B. Takase, K. and Wang, Y. (2007) “Regional groundwater prediction model using automatic parameter calibration SCE method for a coastal plain of Seto Inland Sea,” Water Resources Management, 21, pp. 947–959. Available at: https://doi.org/10.1007/s11269-006-9066-7.
  • Karterakis, S.M. et al. (2007) “Application of linear programming and differential evolutionary optimization methodologies for the solution of coastal subsurface water management problems subject to environmental criteria,” Journal of Hydrology, 342(3–4), pp. 270–282. Available at: https://doi.org/10.1016/j.jhydrol.2007.05.027.
  • Kwanyuen, B. and Fontane, D.G. (1998) “Heuristic branch-and-bound method for ground water development planning,” Journal of Water Resources Planning and Management, 124(3), pp. 140–148. Available at: https://ascelibrary.org/doi/10.1061/%28ASCE%290733-9496%281998%29124%3A3%28140%29.
  • Margat J. and Gun van der, J. (2013) Groundwater around the world: A geographic synopsis. London, U.K.: CRC Press.
  • Maupin, M.A. et al. (2014) “Estimated use of water in the United States in 2010,” U.S. Geological Survey Circular, 1405. Available at: https://doi.org/10.3133/cir1405.
  • Mays, L.W. and Tung, Y.-K. (1992) Hydrosystems Engineering & Management. New York: McGraw-Hill Inc.
  • Parsapour-Moghaddam, P., Abed-Elmdoust, A. and Kerachian, R. (2015) “A heuristic evolutionary game theoretic methodology for conjunctive use of surface and groundwater resources,” Water Resources Management, 29(11), pp. 3905–3918. Available at: https://doi.org/10.1007/s11269-015-1035-6.
  • Redoloza, F. and Li, L. (2020) “A comparison of extremal optimization, differential evolution and particle swarm optimization methods for well placement design in groundwater management,” Mathematical Geosciences, 53(4), pp. 711–735. Available at: https://doi.org/10.1007/s11004-020-09864-3.
  • Santosa, B. and Willy, P. (2011) Metoda metaheuristik konsep dan implementasi [Metaheuristic method concept and implementation]. Surabaya, Indonesia: Guna Widya.
  • Sedki, A. and Ouazar, D. (2010) “Swarm intelligence for groundwater management optimization,” Journal of Hydroinformatics, 13(3), pp. 520–532. Available at: https://doi.org/10.2166/hydro.2010.163.
  • Wang, J., Deng, S. and Lin, M.-C. (2015) “Application of heuristic algorithms on groundwater pumping source identification problems,” 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 858–862. Available at: https://doi.org/10.1109/IEEM.2015.7385770.
  • Wu, J.C. and Zhu, X.B. (2006) “Using the shuffled complex evolution global optimization method to solve groundwater management models,” Frontiers of WWW Research and Development – APWeb 2006. Lecture Notes in Computer Science, 3841. Berlin, Heidelberg: Springer, pp. 986–995. Available at: https://doi.org/10.1007/11610113_105.
  • Zhu, X., Wu, J.C. and Wu, J.F. (2006) “Application of SCE-UA to optimize the management model of groundwater resources in deep aquifers of the Yangtze Delta,” First International MultiSymposiums on Computer and Computational Sciences (IMSCCS'06). Available at: https://doi.org/10.1109/IMSCCS.2006.192.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6206491d-b72b-46f0-a149-31cc5a8435a8
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